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Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs
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Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs

Carl Leake
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Papers citing "Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs"

4 / 4 papers shown
Title
Approximation theory for 1-Lipschitz ResNets
Approximation theory for 1-Lipschitz ResNets
Davide Murari
Takashi Furuya
Carola-Bibiane Schönlieb
81
0
0
17 May 2025
Learning in PINNs: Phase transition, total diffusion, and generalization
Learning in PINNs: Phase transition, total diffusion, and generalization
Sokratis J. Anagnostopoulos
Juan Diego Toscano
Nikolaos Stergiopulos
George Karniadakis
75
11
0
27 Mar 2024
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs
Songming Liu
Zhongkai Hao
Chengyang Ying
Hang Su
Jun Zhu
Ze Cheng
AI4CE
100
17
0
06 Oct 2022
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations
Benjamin Moseley
Andrew Markham
T. Nissen‐Meyer
PINN
91
227
0
16 Jul 2021

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